• Title/Summary/Keyword: Obstacle-avoiding

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Localization of primary user for cognitive radios based on estimation of path-loss exponent (인지무선시스템을 위한 전송 손실 지수 추정 기반의 기 사용자 위치 검출 기법)

  • Anh, Hoang;Koo, Insoo
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.13 no.5
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    • pp.55-63
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    • 2013
  • In cognitive radio networks, acquirement of position information of primary user is very important to secondary network since localization information of primary users can be utilized for improving the spectrum efficiency of secondary network and for avoiding harmful interference to primary users by using proper power control. Among various location methods, Received Signal Strength (RSS)-based localization has been widely used for distance measurements in the location detection process despite its inherent inaccuracy because it can be easily implemented without any additional hardware cost. In the RSS-based localization, the distance is measured by the received signal strength, and distance error can be caused by many factors such as fading, shadowing and obstacle between two nodes. In the paper, therefore we propose a localization scheme based on estimation of path-loss exponent to localize the location of primary users more accurately. Through simulations, it is shown that the proposed scheme can provide less localization error and interference rate to primary users than other schemes.

Learning of Fuzzy Rules Using Fuzzy Classifier System (퍼지 분류자 시스템을 이용한 퍼지 규칙의 학습)

  • Jeong, Chi-Seon;Sim, Gwi-Bo
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.37 no.5
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    • pp.1-10
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    • 2000
  • In this paper, we propose a Fuzzy Classifier System(FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. The FCS is based on the fuzzy controller system combined with machine learning. Therefore the antecedent and consequent of a classifier in FCS are the same as those of a fuzzy rule. In this paper, the FCS modifies input message to fuzzified message and stores those in the message list. The FCS constructs rule-base through matching between messages of message list and classifiers of fuzzy classifier list. The FCS verifies the effectiveness of classifiers using Bucket Brigade algorithm. Also the FCS employs the Genetic Algorithms to generate new rules and modify rules when performance of the system needs to be improved. Then the FCS finds the set of the effective rules. We will verify the effectiveness of the poposed FCS by applying it to Autonomous Mobile Robot avoiding the obstacle and reaching the goal.

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Implementation of a Self Controlled Mobile Robot with Intelligence to Recognize Obstacles (장애물 인식 지능을 갖춘 자율 이동로봇의 구현)

  • 류한성;최중경
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.40 no.5
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    • pp.312-321
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    • 2003
  • In this paper, we implement robot which are ability to recognize obstacles and moving automatically to destination. we present two results in this paper; hardware implementation of image processing board and software implementation of visual feedback algorithm for a self-controlled robot. In the first part, the mobile robot depends on commands from a control board which is doing image processing part. We have studied the self controlled mobile robot system equipped with a CCD camera for a long time. This robot system consists of a image processing board implemented with DSPs, a stepping motor, a CCD camera. We will propose an algorithm in which commands are delivered for the robot to move in the planned path. The distance that the robot is supposed to move is calculated on the basis of the absolute coordinate and the coordinate of the target spot. And the image signal acquired by the CCD camera mounted on the robot is captured at every sampling time in order for the robot to automatically avoid the obstacle and finally to reach the destination. The image processing board consists of DSP (TMS320VC33), ADV611, SAA7111, ADV7l76A, CPLD(EPM7256ATC144), and SRAM memories. In the second part, the visual feedback control has two types of vision algorithms: obstacle avoidance and path planning. The first algorithm is cell, part of the image divided by blob analysis. We will do image preprocessing to improve the input image. This image preprocessing consists of filtering, edge detection, NOR converting, and threshold-ing. This major image processing includes labeling, segmentation, and pixel density calculation. In the second algorithm, after an image frame went through preprocessing (edge detection, converting, thresholding), the histogram is measured vertically (the y-axis direction). Then, the binary histogram of the image shows waveforms with only black and white variations. Here we use the fact that since obstacles appear as sectional diagrams as if they were walls, there is no variation in the histogram. The intensities of the line histogram are measured as vertically at intervals of 20 pixels. So, we can find uniform and nonuniform regions of the waveforms and define the period of uniform waveforms as an obstacle region. We can see that the algorithm is very useful for the robot to move avoiding obstacles.

Utilizing Visual Information for Non-contact Predicting Method of Friction Coefficient (마찰계수의 비접촉 추정을 위한 영상정보 활용방법)

  • Kim, Doo-Gyu;Kim, Ja-Young;Lee, Ji-Hong;Choi, Dong-Geol;Kweon, In-So
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.47 no.4
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    • pp.28-34
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    • 2010
  • In this paper, we proposed an algorithm for utilizing visual information for non-contact predicting method of friction coefficient. Coefficient of friction is very important in driving on road and traversing over obstacle. Our algorithm is based on terrain classification for visual image. The proposed method, non-contacting approach, has advantage over other methods that extract material characteristic of road by sensors contacting road surface. This method is composed of learning group(experiment, grouping material) and predicting friction coefficient group(Bayesian classification prediction function). Every group include previous work of vision. Advantage of our algorithm before entering such terrain can be very useful for avoiding slippery areas. We make experiment on measurement of friction coefficient of terrain. This result is utilized real friction coefficient as prediction method. We show error between real friction coefficient and predicted friction coefficient for performance evaluation of our algorithm.

Detecting and Avoiding Dangerous Area for UAVs Using Public Big Data (공공 빅데이터를 이용한 UAV 위험구역검출 및 회피방법)

  • Park, Kyung Seok;Kim, Min Jun;Kim, Sung Ho
    • KIPS Transactions on Software and Data Engineering
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    • v.8 no.6
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    • pp.243-250
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    • 2019
  • Because of a moving UAV has a lot of potential/kinetic energy, if the UAV falls to the ground, it may have a lot of impact. Because this can lead to human casualities, in this paper, the population density area on the UAV flight path is defined as a dangerous area. The conventional UAV path flight was a passive form in which a UAV moved in accordance with a path preset by a user before the flight. Some UAVs include safety features such as a obstacle avoidance system during flight. Still, it is difficult to respond to changes in the real-time flight environment. Using public Big Data for UAV path flight can improve response to real-time flight environment changes by enabling detection of dangerous areas and avoidance of the areas. Therefore, in this paper, we propose a method to detect and avoid dangerous areas for UAVs by utilizing the Big Data collected in real-time. If the routh is designated according to the destination by the proposed method, the dangerous area is determined in real-time and the flight is made to the optimal bypass path. In further research, we will study ways to increase the quality satisfaction of the images acquired by flying under the avoidance flight plan.